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bioRxiv · 10.1101/2020.02.06.931808

SIAMCAT: user-friendly and versatile machine learning workflows for statistically rigorous microbiome analyses

Wirbel, J.·Zych, K.·Essex, M.·Karcher, N.·Kartal, E.·Salazar, G.·Bork, P.·Sunagawa, S.·Zeller, G.
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Abstract

The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This revealed some biomarkers to be disease-specific, others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de.

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Wirbel, J., Zych, K., Essex, M., Karcher, N., Kartal, E., Salazar, G., Bork, P., Sunagawa, S., Zeller, G.. 2020-02-06. SIAMCAT: user-friendly and versatile machine learning workflows for statistically rigorous microbiome analyses. https://doi.org/10.1101/2020.02.06.931808

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